Full Title: How CEOs Can Lead a Data-Driven Culture
Document Note: Business adoption of data and AI initiatives usally face more cultural and organization barriers than technological ones.
The role of the CEO is to lead by example incorporating data in the decision making process.
Data literacy programs should include concepts and best practices in data analysis in communication but also skills on finding and manipulating data within company.
Forming cross-functional teams (data analytics, business, product, tech) brings diveristy to problem solving.
It is also important to assess and monitor the data/analytics savviness of the leadership team.
Education should focus not only on attitudes and knowledge about data, analytics, and AI, but also on skills for finding and manipulating data at every level, including senior management levels. A survey sponsored by the data analytics vendor Splunk of 1,300 senior executives found that while 81% of the executives agree that data skills are required to become a senior leader in their companies, 67% say they are not comfortable accessing or using data themselves. Seventy three percent felt that data skills are harder to learn than other business skills, and 53% believe they are too old to learn data skills. Effective education initiatives can prove them wrong. (View Highlight)
Leading by example is also important. This requires showcasing leaders who visibly use analytics and AI in internal marketing programs to spread the value of the approach across an organization. Leaders’ exemplary behavior can also include modeling the desired attitude about data and analytics in meetings; leaders should frequently ask, “Do you have data to support that point?” and encourage others to do likewise. (View Highlight)
Promotions and rewards can also encourage change. If those who make effective use of data and analytics get faster promotions and salary increases, others will notice. Of course, this approach requires leadership endorsement and sign-off and execution by Human Resources. (View Highlight)
• Highlighting successes by early adopters and enlisting them help get others engaged;
• Forming cross-functional teams that combine people with backgrounds in data analytics, business, and technology and combining computer science, applied math, engineering, and behavioral economics perspectives to bring diversity and innovate thinking to projects; and
• Launching programs across the organization, including open houses, forums, communities of practice, educational initiatives, and a leadership council – in effect, building marketing capability for analytics and AI within the company that helps create advocates and ambassadors. (View Highlight)
